Self-Learning Server Debugging for Root Cause Correlation
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Solution Overview
Problem
Existing server failure analysis methods treat each error event in isolation, lacking consideration for underlying connections and user actions, leading to time-consuming and costly root cause analysis with inaccurate error reporting.
Innovation Solution
A self-learning process that groups resource data based on learned and programmed context, analyzes interconnectivity of disparate causes, and provides a holistic solution using historical data for improved debugging and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error reporting methods treat each error event in isolation, then the error reporting process is simple and straightforward, but the root cause analysis becomes time-consuming and inaccurate
Solution Approach 1:
The patent combines multiple isolated error events into a unified analysis by creating an error timeline that correlates errors across different components and time periods. This merging approach allows the system to identify patterns and root causes that span multiple components, transforming individual error reports into a comprehensive diagnostic view that improves accuracy while maintaining efficiency.
Solution Approach 2:
The patent introduces an intermediary error timeline structure that mediates between raw error events and root cause analysis. This intermediary layer correlates errors by timing and component relationships, serving as a bridge that organizes disparate error data into a coherent narrative, thereby improving diagnostic accuracy without requiring time-consuming manual analysis of each individual error.
2Loss of information
If traditional error reporting considers each event separately, then the error log structure remains simple, but important underlying connections and user actions are missed
Solution Approach 1:
The patent adds a temporal dimension to error analysis by constructing an error timeline that sequences errors chronologically and correlates them with user actions and system events. This dimensional transformation allows the system to capture relationships between errors that occur at different times and across different components, preserving connection information that would be lost in traditional flat error logs while managing complexity through structured temporal organization.
Solution Approach 2:
The patent segments the error analysis process into distinct components: error collection, timeline construction, correlation analysis, and root cause identification. By dividing the complex analysis task into manageable segments, the system can handle multiple error events and their relationships without overwhelming complexity, while still capturing comprehensive connection information between errors.
3Reliability
If threshold mechanisms promote errors to higher levels, then critical errors are identified, but user actions and system changes are not considered in the debug process
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor error patterns, user actions, and system changes, using this information to refine error promotion decisions. The error timeline captures feedback loops where user debugging actions and system responses are recorded and analyzed, allowing the system to learn from previous errors and improve its identification of critical issues while considering the full context of user interactions and system evolution.
Data Source
AI summary
A method, computer system, and a computer program product are provided for debugging and determining root cause analysis of a server error. Resource data is obtained as relating to a server with error or debugging needs and grouped based on learned and programmed context specific components to determine a root cause analysis for the error or debugging need. An overall solution is then provided to address the server error or debugging need by evaluation a root cause analysis. The overall solution analyzes a plurality of disparate reasons causing the server error or debugging need and providing a single coherent solution based on interconnectivity of the plurality of disparate reasons. The overall solution is updated by obtaining historical data. A recommendation is then provided for the server to resolve the error or the debugging need based on the updated overall solution.